A LoRA fine-tuned and merged version of zai-org/GLM-4.7-Flash, specialized for generating Ilograph Diagram Language (IDL) specifications from natural-language instructions.

The repository includes a system prompt and IDL schema (JSON) use them to reproduce the prompting setup for diagram generation.

Model Details

Developed by: Chris Mijangos (AI student architect at BYU)
Shared by: Brigham Young University (BYU)
Model type: Causal language model (decoder-only), fine-tuned GLM-4.7-Flash with LoRA then merged
Language(s): Primarily English; capabilities depend on base model and fine-tuning data
License: Same as base model; verify zai-org/GLM-4.7-Flash license terms before use
Finetuned from: zai-org/GLM-4.7-Flash

Model Sources

Repository: This model card and weights are shared via the associated Hugging Face repo
Demo: N/A — In construction

Uses

Direct Use

Load the model to generate Ilograph (IDL) diagram specifications from instructions. Use the system prompt and IDL schema in the repository (see “How to Get Started” below). The model is intended for:

Creating IDL diagrams with resources, relationships, and sequences
Iterative, conversational refinement of diagrams (chat-style usage)
Structured YAML outputs following the Ilograph Diagram Language

Out-of-Scope Use

This model is not intended for high-risk or safety-critical applications without further evaluation. Do not use for generating misleading, harmful, or illegal content. Users are responsible for complying with applicable laws and the base model’s license.

Bias, Risks, and Limitations

As with other language models, this model may reflect biases present in the base model and in the fine-tuning data. Outputs should be validated for your use case. No formal bias or safety evaluation is provided with this release.

Recommendations

Users should evaluate the model on their own data and tasks and be aware of potential biases and limitations before deployment.

How to Get Started with the Model

Load the merged fine-tuned model directly from this repository:

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Brigham-Young-University/GLM-4.7-Flash-Ilograph"

tokenizer = AutoTokenizer.from_pretrained(
    model_id,
    trust_remote_code=True,
)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    trust_remote_code=True,
)

inputs = tokenizer("Create 3 resources and create a relation between them", return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Ilograph (IDL) system prompt and schema

The repository includes a system prompt and an IDL schema (JSON). Use the schema to fill in the placeholder in the prompt, then append your instruction. Example system prompt:

You are an expert in the Ilograph Diagram Language (IDL). You have been trained on data that is formatted in the following way:

<insert the schema JSON here>

Your task is to create a valid IDL specification for the diagram. You will be given an instruction of what to create, and you will need to create a valid IDL specification for the diagram.

CRITICAL RULES:
- NEVER use JSON format
- NEVER use Mermaid syntax
- NEVER use any format except ilograph YAML
- Use YAML syntax with proper indentation

Here is the instruction:

The schema is provided in the repository; inject its contents (e.g. as formatted JSON) where indicated above, then add your diagram instruction after “Here is the instruction:”.

Evaluation

No formal evaluation results are provided with this release. Users are encouraged to evaluate the model on their own Ilograph workflows and benchmarks.

Model Card Authors

Chris Mijangos (BYU)

Model Card Contact

For questions about this model card or the model, please open an issue on the associated Hugging Face repository or contact through BYU.

Framework versions

PEFT 0.18.1